To our knowledge, none of the COVID-19 audio datasets have recorded the SARS-CoV-2 variant information for the COVID-19 patients.
However, the Coswara dataset [1]}, an open-access COVID-19 audio dataset, provides the timestamp corresponding to the date each COVID-19 patient contributed their audio data to the dataset. As a work around to obtain information related to variant identity, we match the variant identity to the one which was widespread in the locality on the date of audio data contribution. Based on this, a majority of the COVID-19 patients contributed data to the Coswara dataset during the outbreak of the delta and omicron variants in India.
The Coswara dataset contains audio signals from nine sound categories, collected via crowdsourcing and majorly from India.
This study aims to classify delta and omicron variants based on the claim, set forth by the medical community, that they are lower and upper respiratory diseases respectively. It presents discriminability analysis, both statistically and using LSTM classifiers, with the 9 types of respiratory sounds present in the Coswara dataset.
